Filestructure and Shell Commands
# making a new directory for this lab
mkdir lab05
# changing the directory to that lab
cd lab05
# making subdirectories
mkdir data
mkdir report
mkdir images
ls
# making a README file
touch README.md
# changing directories and downloading the data
cd data
curl -O https://raw.githubusercontent.com/ucb-stat133/stat133-fall-2018/master/data/nba2018-players.csv
ls
# briefly checking out the data
wc nba2018-players.csv
head nba2018-players.csv
tail nba2018-players.csv
NBA Players Data
library (ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readr)
# with "readr" read_csv()
dat <- read_csv("../data/nba2018-players.csv")
## Parsed with column specification:
## cols(
## player = col_character(),
## team = col_character(),
## position = col_character(),
## height = col_integer(),
## weight = col_integer(),
## age = col_integer(),
## experience = col_integer(),
## college = col_character(),
## salary = col_double(),
## games = col_integer(),
## minutes = col_integer(),
## points = col_integer(),
## points3 = col_integer(),
## points2 = col_integer(),
## points1 = col_integer()
## )
Filtering, Slicing, and Selecting
# first three rows
three_rows <- slice(dat, 1:3)
three_rows
## # A tibble: 3 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Al Ho… BOS C 82 245 30 9 Univer… 2.65e7 68
## 2 Amir … BOS PF 81 240 29 11 <NA> 1.20e7 80
## 3 Avery… BOS SG 74 180 26 6 Univer… 8.27e6 55
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# subset rows given a condition
# (height greater than 85 inches)
gt_85 <- filter(dat, height > 85)
gt_85
## # A tibble: 5 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Edy T… CLE C 87 260 24 1 <NA> 5.14e3 1
## 2 Boban… DET C 87 290 28 1 <NA> 7.00e6 35
## 3 Krist… NYK PF 87 240 21 1 <NA> 4.32e6 66
## 4 Roy H… DEN C 86 270 30 8 George… 5.00e6 6
## 5 Alexi… NOP C 86 248 28 6 <NA> 4.60e6 39
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# columns by name
player_height <- select(dat, player, height)
# use slice() to subset the data by selecting the first 5 rows.
slice(dat, 1:5)
## # A tibble: 5 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Al Ho… BOS C 82 245 30 9 Univer… 2.65e7 68
## 2 Amir … BOS PF 81 240 29 11 <NA> 1.20e7 80
## 3 Avery… BOS SG 74 180 26 6 Univer… 8.27e6 55
## 4 Demet… BOS PG 73 201 22 0 Univer… 1.45e6 5
## 5 Geral… BOS SF 79 205 31 9 <NA> 1.41e6 47
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# use slice() to subset the data by selecting rows 10, 15, 20, …, 50.
slice(dat, 2:10*5)
## # A tibble: 9 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Jonas… BOS PF 82 231 29 6 <NA> 5.00e6 78
## 2 Tyler… BOS C 84 253 27 4 Univer… 8.00e6 51
## 3 Derri… CLE PF 80 240 25 5 Univer… 4.02e5 25
## 4 Jorda… CLE SG 78 185 25 1 Univer… 8.75e5 37
## 5 Larry… CLE C 83 235 28 5 Virgin… 2.08e5 5
## 6 Cory … TOR PG 75 193 25 5 Univer… 7.33e6 80
## 7 Jakob… TOR C 84 248 21 0 Univer… 2.70e6 54
## 8 P.J. … TOR SF 78 245 31 5 Univer… 5.30e6 24
## 9 Bradl… WAS SG 77 207 23 4 Univer… 2.21e7 77
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# use slice() to subset the data by selecting the last 5 rows.
slice(dat, -n()+5:n())
## # A tibble: 5 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Marqu… PHO PF 82 233 19 0 Univer… 2.94e6 82
## 2 Ronni… PHO PG 74 190 33 11 Nichol… 4.65e5 14
## 3 T.J. … PHO SF 80 230 23 2 North … 2.13e6 66
## 4 Tyler… PHO PG 70 150 21 0 Univer… 9.18e5 61
## 5 Tyson… PHO C 85 240 34 15 <NA> 1.24e7 47
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# use filter() to subset those players with height less than 70 inches tall.
filter(dat, height < 70)
## # A tibble: 2 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Isaia… BOS PG 69 185 27 5 Univer… 6.59e6 76
## 2 Kay F… CLE PG 69 176 21 0 Oaklan… 5.43e5 42
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# use filter() to subset rows of Golden State Warriors (‘GSW’).
filter(dat, team == "GSW")
## # A tibble: 16 x 15
## player team position height weight age experience college salary
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl>
## 1 Ander… GSW C 82 273 34 12 <NA> 1.55e6
## 2 Andre… GSW SF 78 215 33 12 Univer… 1.11e7
## 3 Damia… GSW C 84 245 21 0 Vander… 1.17e6
## 4 David… GSW C 81 250 36 13 Xavier… 1.55e6
## 5 Draym… GSW PF 79 230 26 4 Michig… 1.53e7
## 6 Ian C… GSW SG 75 175 25 3 Belmon… 1.02e6
## 7 James… GSW PF 81 230 24 2 Univer… 9.80e5
## 8 JaVal… GSW C 84 270 29 8 Univer… 1.40e6
## 9 Kevin… GSW PF 81 240 28 9 Univer… 2.65e7
## 10 Kevon… GSW C 81 220 20 1 Univer… 1.18e6
## 11 Klay … GSW SG 79 215 26 5 Washin… 1.67e7
## 12 Matt … GSW SF 79 226 36 13 Univer… 3.83e5
## 13 Patri… GSW SG 79 185 21 0 Univer… 5.43e5
## 14 Shaun… GSW PG 79 192 31 11 <NA> 5.78e6
## 15 Steph… GSW PG 75 190 28 7 Davids… 1.21e7
## 16 Zaza … GSW C 83 270 32 13 <NA> 2.90e6
## # ... with 6 more variables: games <int>, minutes <int>, points <int>,
## # points3 <int>, points2 <int>, points1 <int>
# use filter() to subset rows of GSW centers (‘C’).
filter(dat, team == "GSW", position == "C")
## # A tibble: 6 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Ander… GSW C 82 273 34 12 <NA> 1.55e6 14
## 2 Damia… GSW C 84 245 21 0 Vander… 1.17e6 10
## 3 David… GSW C 81 250 36 13 Xavier… 1.55e6 68
## 4 JaVal… GSW C 84 270 29 8 Univer… 1.40e6 77
## 5 Kevon… GSW C 81 220 20 1 Univer… 1.18e6 53
## 6 Zaza … GSW C 83 270 32 13 <NA> 2.90e6 70
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# use filter() and then select(), to subset rows of lakers (‘LAL’), and then display their names.
select(filter(dat, team == "LAL"), player)
## # A tibble: 14 x 1
## player
## <chr>
## 1 Brandon Ingram
## 2 Corey Brewer
## 3 D'Angelo Russell
## 4 David Nwaba
## 5 Ivica Zubac
## 6 Jordan Clarkson
## 7 Julius Randle
## 8 Luol Deng
## 9 Metta World Peace
## 10 Nick Young
## 11 Tarik Black
## 12 Thomas Robinson
## 13 Timofey Mozgov
## 14 Tyler Ennis
# use filter() and then select(), to display the name and salary, of GSW point guards
select(filter(dat, team == "GSW", position == "PG"), player, salary)
## # A tibble: 2 x 2
## player salary
## <chr> <dbl>
## 1 Shaun Livingston 5782450
## 2 Stephen Curry 12112359
# find how to select the name, age, and team, of players with more than 10 years of experience, making 10 million dollars or less.
select(filter(dat, experience > 10, salary <= 10000000), player, age, team)
## # A tibble: 36 x 3
## player age team
## <chr> <int> <chr>
## 1 Andrew Bogut 32 CLE
## 2 Dahntay Jones 36 CLE
## 3 Deron Williams 32 CLE
## 4 James Jones 36 CLE
## 5 Kyle Korver 35 CLE
## 6 Richard Jefferson 36 CLE
## 7 Jose Calderon 35 ATL
## 8 Kris Humphries 31 ATL
## 9 Mike Dunleavy 36 ATL
## 10 Jason Terry 39 MIL
## # ... with 26 more rows
# find how to select the name, team, height, and weight, of rookie players, 20 years old, displaying only the first five occurrences (i.e. rows)
slice(select(filter(dat, experience == 0, age == 20), player, team, height, weight), 1:5)
## # A tibble: 5 x 4
## player team height weight
## <chr> <chr> <int> <int>
## 1 Jaylen Brown BOS 79 225
## 2 Henry Ellenson DET 83 245
## 3 Stephen Zimmerman ORL 84 240
## 4 Dejounte Murray SAS 77 170
## 5 Chinanu Onuaku HOU 82 245
Adding New Variables and Reordering Rows
# creating a small data frame step by step
gsw <- filter(dat, team == 'GSW')
gsw <- select(gsw, player, height, weight)
gsw <- slice(gsw, c(4, 8, 10, 14, 15))
gsw
## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 David West 81 250
## 2 JaVale McGee 84 270
## 3 Kevon Looney 81 220
## 4 Shaun Livingston 79 192
## 5 Stephen Curry 75 190
# (temporarily) add a column with the ratio height / weight:
mutate(gsw, height / weight)
## # A tibble: 5 x 4
## player height weight `height/weight`
## <chr> <int> <int> <dbl>
## 1 David West 81 250 0.324
## 2 JaVale McGee 84 270 0.311
## 3 Kevon Looney 81 220 0.368
## 4 Shaun Livingston 79 192 0.411
## 5 Stephen Curry 75 190 0.395
# can also add name of col
mutate(gsw, ht_wt = height / weight)
## # A tibble: 5 x 4
## player height weight ht_wt
## <chr> <int> <int> <dbl>
## 1 David West 81 250 0.324
## 2 JaVale McGee 84 270 0.311
## 3 Kevon Looney 81 220 0.368
## 4 Shaun Livingston 79 192 0.411
## 5 Stephen Curry 75 190 0.395
#need to assign in order to change
gsw2 <- mutate(gsw, ht_m = height * 0.0254, wt_kg = weight * 0.4536)
gsw2
## # A tibble: 5 x 5
## player height weight ht_m wt_kg
## <chr> <int> <int> <dbl> <dbl>
## 1 David West 81 250 2.06 113.
## 2 JaVale McGee 84 270 2.13 122.
## 3 Kevon Looney 81 220 2.06 99.8
## 4 Shaun Livingston 79 192 2.01 87.1
## 5 Stephen Curry 75 190 1.90 86.2
# order rows by height (increasingly) - by default
arrange(gsw, height)
## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 Stephen Curry 75 190
## 2 Shaun Livingston 79 192
## 3 David West 81 250
## 4 Kevon Looney 81 220
## 5 JaVale McGee 84 270
# order rows by height (decreasingly)
arrange(gsw, desc(height))
## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 JaVale McGee 84 270
## 2 David West 81 250
## 3 Kevon Looney 81 220
## 4 Shaun Livingston 79 192
## 5 Stephen Curry 75 190
# order rows by height, and then weight
arrange(gsw, height, weight)
## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 Stephen Curry 75 190
## 2 Shaun Livingston 79 192
## 3 Kevon Looney 81 220
## 4 David West 81 250
## 5 JaVale McGee 84 270
Adding New Variables and Reordering Rows: Your Turn
# using the data frame gsw, add a new variable product with the product of height and weight.
mutate(gsw, h_w = height*weight)
## # A tibble: 5 x 4
## player height weight h_w
## <chr> <int> <int> <int>
## 1 David West 81 250 20250
## 2 JaVale McGee 84 270 22680
## 3 Kevon Looney 81 220 17820
## 4 Shaun Livingston 79 192 15168
## 5 Stephen Curry 75 190 14250
# create a new data frame gsw3, by adding columns log_height and log_weight with the log transformations of height and weight.
gsw3 <- mutate(gsw, log_height = log(height), log_weight = log(weight))
gsw3
## # A tibble: 5 x 5
## player height weight log_height log_weight
## <chr> <int> <int> <dbl> <dbl>
## 1 David West 81 250 4.39 5.52
## 2 JaVale McGee 84 270 4.43 5.60
## 3 Kevon Looney 81 220 4.39 5.39
## 4 Shaun Livingston 79 192 4.37 5.26
## 5 Stephen Curry 75 190 4.32 5.25
# use the original data frame to filter() and arrange() those players with height less than 71 inches tall, in increasing order.
arrange(filter(dat, height < 71), height)
## # A tibble: 4 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Isaia… BOS PG 69 185 27 5 Univer… 6.59e6 76
## 2 Kay F… CLE PG 69 176 21 0 Oaklan… 5.43e5 42
## 3 Pierr… DAL PG 70 180 25 0 Baylor… 1.05e5 8
## 4 Tyler… PHO PG 70 150 21 0 Univer… 9.18e5 61
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>
# display the name, team, and salary, of the top-5 highest paid players
slice(select(arrange(dat, desc(salary)),player, team, salary), 1:5)
## # A tibble: 5 x 3
## player team salary
## <chr> <chr> <dbl>
## 1 LeBron James CLE 30963450
## 2 Al Horford BOS 26540100
## 3 DeMar DeRozan TOR 26540100
## 4 Kevin Durant GSW 26540100
## 5 James Harden HOU 26540100
# display the name, team, and points3, of the top 10 three-point players
slice(select(arrange(dat, desc(points3)),player, team, points3), 1:10)
## # A tibble: 10 x 3
## player team points3
## <chr> <chr> <int>
## 1 Stephen Curry GSW 324
## 2 Klay Thompson GSW 268
## 3 James Harden HOU 262
## 4 Eric Gordon HOU 246
## 5 Isaiah Thomas BOS 245
## 6 Kemba Walker CHO 240
## 7 Bradley Beal WAS 223
## 8 Damian Lillard POR 214
## 9 Ryan Anderson HOU 204
## 10 J.J. Redick LAC 201
# create a data frame gsw_mpg of GSW players, that contains variables for player name, experience, and min_per_game (minutes per game), sorted by min_per_game (in descending order)
gsw_mpg <- select(arrange(filter(mutate(dat, min_per_game = minutes/games), team == "GSW"), desc(min_per_game)), player, experience, min_per_game)
gsw_mpg
## # A tibble: 16 x 3
## player experience min_per_game
## <chr> <int> <dbl>
## 1 Klay Thompson 5 34.0
## 2 Stephen Curry 7 33.4
## 3 Kevin Durant 9 33.4
## 4 Draymond Green 4 32.5
## 5 Andre Iguodala 12 26.3
## 6 Matt Barnes 13 20.5
## 7 Zaza Pachulia 13 18.1
## 8 Shaun Livingston 11 17.7
## 9 Patrick McCaw 0 15.1
## 10 Ian Clark 3 14.8
## 11 David West 13 12.6
## 12 JaVale McGee 8 9.60
## 13 James Michael McAdoo 2 8.79
## 14 Damian Jones 0 8.5
## 15 Kevon Looney 1 8.43
## 16 Anderson Varejao 12 6.57
Summarize and Group Operations
# average salary of NBA players
summarise(dat, avg_salary = mean(salary))
## # A tibble: 1 x 1
## avg_salary
## <dbl>
## 1 5804697.
# some stats for salary (dplyr)
summarise(
dat,
min = min(salary),
median = median(salary),
avg = mean(salary),
max = max(salary)
)
## # A tibble: 1 x 4
## min median avg max
## <dbl> <dbl> <dbl> <dbl>
## 1 5145 3000000 5804697. 30963450
# average salary, grouped by team
summarise(
group_by(dat, team),
avg_salary = mean(salary)
)
## # A tibble: 30 x 2
## team avg_salary
## <chr> <dbl>
## 1 ATL 5494447.
## 2 BOS 6127673.
## 3 BRK 4011351.
## 4 CHI 5781368.
## 5 CHO 5531548.
## 6 CLE 7069699.
## 7 DAL 5157128.
## 8 DEN 4648719.
## 9 DET 6871632.
## 10 GSW 6265160.
## # ... with 20 more rows
# average salary, grouped by position
summarise(
group_by(dat, position),
avg_salary = mean(salary)
)
## # A tibble: 5 x 2
## position avg_salary
## <chr> <dbl>
## 1 C 6529906.
## 2 PF 5801127.
## 3 PG 5601217.
## 4 SF 6042455.
## 5 SG 5114178.
# average weight and height, by position, displayed in desceding order by average height:
arrange(
summarise(
group_by(dat, position),
avg_height = mean(height),
avg_weight = mean(weight)),
desc(avg_height)
)
## # A tibble: 5 x 3
## position avg_height avg_weight
## <chr> <dbl> <dbl>
## 1 C 83.2 251.
## 2 PF 81.4 235.
## 3 SF 79.5 220.
## 4 SG 77.0 204.
## 5 PG 74.3 189.
Summarize and Group Operations: Your Turn
# use summarise() to get the largest height value.
summarise(dat, max_height = max(height))
## # A tibble: 1 x 1
## max_height
## <dbl>
## 1 87
# use summarise() to get the standard deviation of points3.
summarise(dat, sd_of_3pointers = sd(points3))
## # A tibble: 1 x 1
## sd_of_3pointers
## <dbl>
## 1 55.1
# use summarise() and group_by() to display the median of three-points, by team.
summarise(
group_by(dat, team),
median_of_3pointers = median(points3)
)
## # A tibble: 30 x 2
## team median_of_3pointers
## <chr> <dbl>
## 1 ATL 32
## 2 BOS 46
## 3 BRK 36
## 4 CHI 28.5
## 5 CHO 13
## 6 CLE 26.5
## 7 DAL 18
## 8 DEN 46
## 9 DET 28
## 10 GSW 10.5
## # ... with 20 more rows
# display the average triple points by team, in ascending order, of the bottom-5 teams (worst 3pointer teams)
slice(
arrange(
summarise(
group_by(dat, team),
average_3pointers = mean(points3)
),
average_3pointers
),
1:5
)
## # A tibble: 5 x 2
## team average_3pointers
## <chr> <dbl>
## 1 NOP 32.4
## 2 PHO 33.5
## 3 ORL 34.3
## 4 SAC 35.1
## 5 CHI 35.3
# obtain the mean and standard deviation of age, for Power Forwards, with 5 and 10 years (including) years of experience.
summarise(
filter(dat, position == "PF", experience >= 5 && experience <= 10),
mean = mean(age),
sd = sd(age)
)
## # A tibble: 1 x 2
## mean sd
## <dbl> <dbl>
## 1 26.1 4.11
ggplot(): Your Turn
# Use the data frame gsw to make a scatterplot of height and weight.
ggplot(data = gsw, aes(x = height, y = weight)) + geom_point()
# Find out how to make another scatterplot of height and weight, using geom_text() to display the names of the players.
ggplot(data = gsw, aes(x = height, y = weight)) + geom_point() + geom_text(label = gsw$player)
# Get a scatter plot of height and weight, for ALL the warriors, displaying their names with geom_label().
ggplot(data = filter(dat, team == "GSW"), aes(x = height, y = weight)) + geom_point() + geom_text(label = filter(dat, team == "GSW")$player)
# Get a density plot of salary (for all NBA players).
ggplot(data = dat, aes(x = salary)) + geom_density()
# Get a histogram of points2 with binwidth of 50 (for all NBA players).
ggplot(data = dat, aes(x = points2)) + geom_histogram(binwidth = 50)
# Get a barchart of the position frequencies (for all NBA players).
ggplot(data = dat, aes(x = position)) + geom_bar()
# Make a scatterplot of experience and salary of all Centers, and use geom_smooth() to add a regression line.
ggplot(data = filter(dat, position == "C"), aes(x = experience, y = salary)) + geom_point() + geom_smooth(method = lm)
# Repeat the same scatterplot of experience and salary of all Centers, but now use geom_smooth() to add a loess line (i.e. smooth line).
ggplot(data = filter(dat, position == "C"), aes(x = experience, y = salary)) + geom_point() + geom_smooth(method = loess)
Faceting
# scatterplot by position
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point() +
facet_wrap(~ position)
# scatterplot by position (vertical)
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point(aes(color = position), alpha = 0.7) +
facet_grid(~ position) +
geom_smooth(method = loess)
# scatterplot by position (horizontal)
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point(aes(color = position), alpha = 0.7) +
facet_grid(position ~ .) +
geom_smooth(method = loess)
Faceting: Your Turn
# Make scatterplots of experience and salary faceting by position
ggplot(data = dat, aes(x = experience, y = salary)) + geom_point() + facet_wrap(~position)
# Make scatterplots of experience and salary faceting by team
ggplot(data = dat, aes(x = experience, y = salary)) + geom_point() + facet_wrap(~team)
# Make density plots of age faceting by team
ggplot(data = dat, aes(x = age)) + geom_density() + facet_wrap(~team)
# Make scatterplots of height and weight faceting by position
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + facet_wrap(~position)
# Make scatterplots of height and weight, with a 2-dimensional density, geom_density2d(), faceting by position
ggplot(data = dat, aes(x = height, y = weight)) + geom_density2d() + facet_wrap(~position)
# Make a scatterplot of experience and salary for the Warriors, but this time add a layer with theme_bw() to get a simpler background
ggplot(data = filter(dat, team == "GSW"), aes(x = experience, y = salary)) + geom_point() + theme_bw()
# Repeat any of the previous plots but now adding a leyer with another theme e.g. theme_minimal(), theme_dark(), theme_classic()
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + facet_wrap(~position) + theme_minimal()
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + facet_wrap(~position) + theme_dark()
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + facet_wrap(~position) + theme_classic()
More Shell Commands
# Move inside the images/ directory of the lab.
cd ~/Desktop/hw-stat133/lab05/images
# List the contents of this directory.
ls
# Now list the contents of the directory in long format.
ls -1
# How would you list the contents in long format, by time?
ls -1 -t
# How would you list the contents displaying the results in reverse (alphabetical)? order without changing your current directory
ls -1 -r
#create a directory copies at the parent level (i.e. lab05/).
mkdir copies
# Copy one of the PNG files to the copies folder.
cp images/unnamed-chunk-9-2.png copies
# Use the wildcard * to copy all the .png files in the directory copies.
cp images/*.png copies
# Change to the directory copies.
cd copies
# Use the command mv to rename some of your PNG files.
mv unnamed-chunk-8-1.png image1.png
mv unnamed-chunk-8-2.png image2.png
mv unnamed-chunk-8-3.png image3.png
mv unnamed-chunk-8-4.png image4.png
# Change to the report/ directory.
cd ~/Desktop/hw-stat133/lab05/report
# From within report/, find out how to rename the directory copies as copy-files.
mv ~/Desktop/hw-stat133/lab05/copies ~/Desktop/hw-stat133/lab05/copy_files
# From within report/, delete one or two PNG files in copy-files.
rm ~/Desktop/hw-stat133/lab05/copy_files/image1.png
# From within report/, find out how to delete the directory copy-files.
rm -r ~/Desktop/hw-stat133/lab05/copy_files
